Abstract
To satisfy practical requirements of high real-time accuracy and low computational complexity of synthetic aperture radar (SAR) image ship small target detection, this paper proposes a small ship target detection method based on the improved You Only Look Once Version 3 (YOLOv3). The main contributions of this study are threefold. First, the feature extraction network of the original YOLOV3 algorithm is replaced with the VGG16 network convolution layer. Second, general convolution is transformed into depthwise separable convolution, thereby reducing the computational cost of the algorithm. Third, a residual network structure is introduced into the feature extraction network to reuse the shallow target feature information, which enhances the detailed features of the target and ensures the improvement in accuracy of small target detection performance. To evaluate the performance of the proposed method, many experiments are conducted on public SAR image datasets. For ship targets with complex backgrounds and small ship targets in the SAR image, the effectiveness of the proposed algorithm is verified. Results show that the accuracy and recall rate improved by 5.31% and 2.77%, respectively, compared with the original YOLOV3. Furthermore, the proposed model not only significantly reduces the computational effort, but also improves the detection accuracy of ship small target.
Highlights
You Only Look Once Version 3 (YOLOv3) was originally used for target detection on visible images, and it needs to make some adaptive improvement in its network when it is used for object detection in synthetic aperture radar (SAR) images
The experiment in the third part of this paper verifies that compared with the original YOLOv3, the proposed method improves the accuracy of SAR image small target detection
Column a is the original image, column b is the detection result obtained by using the original YOLOv3 algorithm, and column c is the detection result obtained by the model in this paper
Summary
Target detection algorithms based on deep learning can be categorized into two types: two-stage and one-stage algorithms. After the extraction of image features, the two-stage detection method first inputs the feature information into a regional proposal network (RPN), which conducts the initial classification of targets. Because the calculation of the final detection comprises two stages, the real-time performance is poor. The one-stage detection algorithm does not require the RPN module as it uses the extracted feature information to directly conduct classification and position regression calculations for the target. These algorithms are suitable for accomplishing real-time detection
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